Abstract
Although the Multihoist Scheduling Problem (MHSP) can be detailed as a job-shop configuration, the MHSP has additional constraints. Such constraints increase the difficulty and complexity of the schedule. Operation conditions in chemical processes are certainly different from other types of processes. Therefore, in order to model the real-world environment on a chemical production process, a simulation model is built and it emulates the feasibility requirements of such a production system. The results of the model, i.e., the makespan and the workload of the most loaded tank, are necessary for providing insights about which schedule on the shop floor should be implemented. A new biobjective optimization method is proposed, and it uses the results mentioned above in order to build new scenarios for the MHSP and to solve the aforementioned conflicting objectives. Various numerical experiments are shown to illustrate the performance of this new experimental technique, i.e., the simulation optimization approach. Based on the results, the proposed scheme tackles the inconvenience of the metaheuristics, i.e., lack of diversity of the solutions and poor ability of exploitation. In addition, the optimization approach is able to identify the best solutions by a distance-based ranking model and the solutions located in the first Pareto-front layer contributes to improve the search process of the aforementioned scheme, against other algorithms used in the comparison.
Highlights
Some chemical-production systems use tanks containing treatment baths in order to generate finished products.ose treatment baths can contain rinsing, acid, or electroplating solutions. is kind of production system is necessary when a product needs to be treated with a specific treatment bath to enhance its mechanical, electrical, or esthetic properties. e products are soaked in each tank according to a given sequence [1]
Paretot−1 ⟵ Select best individuals from FitDt−1 σ0 ⟵ Central permutation computing from Paretot−1 Vt−1 ⟵ Distance computing from Dt−1 and σ0 ∅ ⟵ Spread parameter computing from Vt−1 Dst ⟵ Sampling from Vt−1 pt−1 ⟵ p matrix computing from Paretot−1 Dpt ⟵ Sampling from pt−1 qt−1 ⟵ q matrix computing from Paretot−1 Dqt ⟵ Sampling from qt−1 Dt ⟵ Replacement all old members with new offspring t: t + 1 Until stopping criterion is met ALGORITHM 1: Pseudocode MHEDA framework
Research is paper considers the Multihoist Scheduling Problem (MHSP). It tries to determine the sequence of operations for each hoist to perform so that some performance metric is optimized. e MHEDA scheme is proposed for tackling the problem and simulating a solution
Summary
Some chemical-production systems use tanks containing treatment baths in order to generate finished products. In order to simultaneously schedule jobs and hoists, this study proposes a solution for the MHSP. E Generalized Mallows Distribution (GMD) process, detailed in Fligner and Verducci [9], and Fligner and Verducci [10], explains the procedure to get the decomposition of the distance between permutations and thereby generate a new offspring. With this strategy, the inconvenience of probability models in permutation-based problems is solved. Based on the concept of properly modeling the main variables that intervene in the performance of the process has been a priority in the solution of real-world scheduling problems [11]. Simulation model emulates the facility being studied, while MHEDA is used to get the best solution
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